Why trust climate models? It’s a matter of simple science

How climate scientists test, test again, and use their simulation tools.

Ice, on the rocks

While Tony Del Genio has his head in the clouds and outward into the Solar System beyond, Penn State glaciologist Richard Alley stands on ice sheets miles thick, thinking about what’s going on beneath his feet. Instead of trying to model the whole climate system, he’s focused on the behavior of valley glaciers and ice sheets. “An ice sheet is a two-mile-thick, one-continent-wide pile of old snow squeezed to ice under the weight of more snow and spreading under its own weight,” Alley told Ars. “The impetus for flow is essentially the excess pressure inside the ice compared to outside, and it's usually quantified as being the product of the ice density, gravitational acceleration, thickness of ice above the point you're talking about, and surface slope.”

Ice sheet models use the equations that describe that flow of ice to simulate how the ice sheet changes over time in response to outside factors. The size of an ice sheet, like a bank account, is determined by the balance of gains and losses. Increase the amount of melting going on at the edges of the ice sheet and it will shrink. Increase the amount of snowfall over the cold, central region of the ice sheet and it will grow. Lubricate the base of the ice sheet with liquid water, and it may flow faster to the sea, causing an overall loss of ice.

These models are complex and detailed enough that they’re usually run on their own rather than within a climate model that is already busy trying to handle the rest of the planet. Depending on the experiment being run with the model, climate conditions simulated by another model might be imported or a simpler, pre-determined scenario might suffice.

Like global climate models, ice sheet models can also be evaluated against what we know about the past. “Does the model put ice in places that ice was known to have been and not in places where ice was absent?” Alley said. “Are the fluctuations of ice in response to orbital forcing in the past configuration consistent with the reconstructed changes in sea level based on coastal indicators or isotopic composition of the ocean as inferred from ratios in particular shells in sediment cores?”

All this work eventually contributes to our understanding of how the ice sheet is likely to behave in the future. “For these projections to be reliable, we want to see similar behavior in a range of models, from simple to complex, run by different groups, and to understand physically why the models are producing the results they do; we're especially confident if the paleoclimatic record shows a similar response to similar forcings in the past, and if we see the projected behavior emerging now in response to the recent human and natural forcings,” Alley said. “With all four—physical understanding, agreement in a range of models, observed in paleo and emerging now—we're pretty confident; with fewer, less so.”

Along with providing better estimates of how ice sheets will contribute to sea level rise, ice sheet models also help generate research questions. By revealing the biggest sources of uncertainty, models can point to the types of measurements and research that will yield the greatest bang for the buck.

Enlarge/ Simulation of ice sheet elevation at the peak of the last ice age using the Parallel Ice Sheet Model and the ECHAM5 climate model.

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There’s another way in which these climate models are probed—by comparing them with each other. Since there are so many groups of researchers independently building their own models to approximate the climate system, the similarities and differences of their simulations can be illuminating.

Observational data is necessarily limited, but every single thing in a model can be examined. That makes model-to-model comparison more of an apples-to-apples affair when they’re run using the same inputs (like greenhouse gas emissions scenarios). The cause of a poor match between some portion of a model and reality isn’t always obvious, whereas it could jump out when the results are compared to those produced by another model.

There are many such “model intercomparison projects,” including ones focused on atmospheric models, paleoclimate simulations, or geoengineering research. The largest is the Coupled Model Intercomparison Project (CMIP), which has become an important resource for the Intergovernmental Panel on Climate Change reports. What started in 1995 as a simple project blossomed into an enormously useful organizing force for an abundance of research.

Each phase of the project includes a set of experiments chosen by the modeling community. In the latest round, for example, the models have been investigating short-term, decadal predictions, the way clouds change in a warming climate, and a new technique for making comparisons between model results and atmospheric data from satellites.

Apart from helping research groups improve their models, CMIP also makes climate simulations from all the models involved accessible to other researchers. Interested in the future behavior of Himalayan glaciers? Or the economic impact of changes in precipitation over the US? Simulations from a variety of models for a range of emissions scenarios are conveniently available in one place and in standardized formats. In a way, that coordination also increases the value of the studies that use this data. If three different studies on species migration caused by climate change each used arbitrarily different scenarios for the future, comparing their results could be more difficult.

The most visible product of CMIP has probably been its contribution to the IPCC reports. When the reports show model ensembles (many simulations averaged together), they’re pulling from the CMIP collection. Rather than choosing a preferred model, the IPCC essentially works from the average of all of them, while the range of their results is used as an indicator of uncertainty. In this way, the work of independent modeling groups around the world is aggregated to help inform policy makers.

No crystal ball—but no magic 8 ball, either

If you only tune in to public arguments about climate change or read about the latest study that uses climate models, it’s easy to lose sight of the truly extraordinary achievement those models represent. As Andrew Weaver told Ars, “What is so remarkable about these climate models is that it really shows how much we know about the physics and chemistry of the atmosphere, because they’re ultimately driven by one thing—that is, the Sun. So you start with these equations, and you start these equations with a world that has no moisture in the atmosphere that just has seeds on land but has no trees anywhere, that has an ocean that has a constant temperature and a constant amount of salt in it, and it has no sea ice, and all you do is turn it on. [Flick on] the Sun, and you see this model predict a system that looks so much like the real world. It predicts storm tracks where they should be, it predicts ocean circulation where it should be, it grows trees where it should, it grows a carbon cycle—it really is remarkable.”

But climate scientists know models are just scientific tools—nothing more. In studying the practices of climate modeling groups, Steve Easterbrook saw this firsthand. “One of the most common uses of the models is to look for surprises—places where the model does something unexpected, primarily as a way of probing the boundaries of what we know and what we can simulate," he said. "The models are perfectly suited for this. They get the basic physical processes right but often throw up surprises in the complex interactions between different parts of the Earth system. It is in these areas where the scientific knowledge is weakest. So the models help guide the scientific process."

“So I have tremendous respect for what the models are able to do (actually, I'd say it's mind-blowing), but that's a long way from saying that any one model can give accurate forecasts of climate change in the future on any timescale," Easterbrook continued. “I'm particularly impressed by how much this problem is actively acknowledged and discussed in the climate modeling community and how cautious the modelers are in working to avoid any possible over-interpretation of model results.”

“One of the biggest sources of confidence in the models is that they give results that are broadly consistent with one another (despite some very different scientific choices in different models), and they give results that are consistent with the available data and current theory,” Easterbrook said. And while they're being developed, the rest of the broad field of climate science is hard at work gathering more data and developing our theoretical understanding of the climate system—information that will inform the next generation of models.

The guiding principle in modeling of any kind was summarized by George E.P. Box when he wrote that “all models are wrong, but some are useful.” Climate scientists work hard to ensure that their models are useful, whether to understand what happened in the past or what could happen in the future.

Every projection showing multiple scenarios for future greenhouse gas emissions illustrates the present moment as a constantly shifting crossroads—the point where all future paths diverge, with their course determined using climate models. Armed with that map, we get to decide which of the possible paths we are going to make reality. The more we understand about the climate system and the more realistically climate models behave, the more detailed that map becomes. There’s always more to work out, but we’ve already advanced well past the stage where we need to ask for directions.

Promoted Comments

Question: Is there any crude model that can run on consumer level hardware? Some coarse grid highly parametrized toy earth?

I've an urge to tinker.

If you want to play with an OLD model, EDGCM http://edgcm.columbia.edu/ is a fairly user-friendly (runs from GUI, processes output and makes maps for you) - That is a variant of the 1980's NASA GISS model - it runs 8x10 degree grid cells (current models run in the vicinity of 1 degree) with a fairly simple ocean model. It comes with some data and you can alter some of the variables or input different atmospheric trends. *

Its single threaded and takes about a day to do 150 years on a westmere Mac Pro - but you can run several instances at once to fiddle with parameters.

The NASA GISS site seems to no longer link to the intermediate models between that one and the current Model E - I've actually run them on my machine but they took a lot more time tinkering (just gave you fortran code), required compiling and figuring out what to do with the output once you have it. And the run time goes up a lot.

But if you want to tinker, use EdGCM, its designed to be approachable and useable without mammoth investments of time or computing horsepower.

*Some details may be slightly off, its been a few years since I used it.